Prediction of the Extent of Blood-Brain Barrier Transport Using Machine Learning and Integration into the LeiCNS-PK3.0 Model.

ABSTRACT:

Introduction

The unbound brain-to-plasma partition coefficient (K_p,uu,BBB) is an essential parameter for predicting central nervous system (CNS) drug disposition using physiologically-based pharmacokinetic (PBPK) modeling. K_p,uu,BBB values for specific compounds are however often unavailable, and are moreover time consuming to obtain experimentally. The aim of this study was to develop a quantitative structure-property relationship (QSPR) model to predict the K_p,uu,BBB and to demonstrate how QSPR-model predictions can be integrated into a physiologically-based pharmacokinetic model for the CNS.

Methods

Rat K_p,uu,BBB values were obtained for 98 compounds from literature or in house historical data. For all compounds, 2D and 3D physi

SUBMITTER: Gulave B 

PROVIDER: S-EPMC11880073 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature